Summarizing and providing my notes on the paper - A review of possible effects of cognitive biases on interpretation of rule-based machine learning models by Kliegr, Bahník and Fürnkranz (2021)
Summarizing and providing my notes on the paper - Programmatically Interpretable Reinforcement Learning by Verma et. al. (2018)
Summarizing and providing my notes on the paper - Distilling Deep Reinforcement Learning Policies in Soft Decision Trees by Coppens et. al. (2019)
Summarizing and providing my notes on the paper - Contrastive explanations for reinforcement learning in terms of expected consequences by van der Waa et. al. (2018)
Summarizing and providing my notes on the paper - Graying the black box: Understanding DQNs by Zahavy, Ben-Zrihem & Mannor (2016)
Summarizing and providing my notes on the paper - Improving Robot Controller Transparency Through Autonomous Policy Explanation by Hayes & Shah (2017)
Summarizing and providing my notes on the paper - Policy Distillation by Rusu et. al. (2016)
Summarizing and providing my notes on the paper - Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees by Liu et. al. (2018)
Summarizing and providing my notes on the paper - Explainable Reinforcement Learning Through a Causal Lens by Madumal et. al. (2020)